Re: [PR] feat: introduce sklearn testing operator [texera]
aglinxinyuan commented on PR #4211: URL: https://github.com/apache/texera/pull/4211#issuecomment-3903349640 Currently, we do not have a way to test each operator individually. We can discuss whether to include test cases for each operator in the next group meeting. This operator follows the same design as the sklearn prediction operator, where the user specifies the column for the target value (i.e., y), and we assume the remaining columns are input features (X). -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
Re: [PR] feat: introduce sklearn testing operator [texera]
chenlica commented on PR #4211: URL: https://github.com/apache/texera/pull/4211#issuecomment-3903341129 I know this PR was already merged. Just curious: can we add test cases for this new operator? Also how to specify the columns used by each model in order to do testing? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
Re: [PR] feat: introduce sklearn testing operator [texera]
aglinxinyuan merged PR #4211: URL: https://github.com/apache/texera/pull/4211 -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
Re: [PR] feat: introduce sklearn testing operator [texera]
aglinxinyuan commented on code in PR #4211:
URL: https://github.com/apache/texera/pull/4211#discussion_r2806780450
##
common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/sklearn/testing/SklearnTestingOpDesc/SklearnTestingOpDesc.scala:
##
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.sklearn.testing.SklearnTestingOpDesc
+
+import com.fasterxml.jackson.annotation.{JsonProperty, JsonPropertyDescription}
+import com.kjetland.jackson.jsonSchema.annotations.JsonSchemaTitle
+import org.apache.texera.amber.core.tuple.{AttributeType, Schema}
+import org.apache.texera.amber.core.workflow.{InputPort, OutputPort,
PortIdentity}
+import org.apache.texera.amber.operator.PythonOperatorDescriptor
+import org.apache.texera.amber.operator.metadata.annotations.{
+ AutofillAttributeName,
+ AutofillAttributeNameOnPort1
+}
+import org.apache.texera.amber.operator.metadata.{OperatorGroupConstants,
OperatorInfo}
+
+class SklearnTestingOpDesc extends PythonOperatorDescriptor {
+ @JsonSchemaTitle("Model Attribute")
+ @JsonProperty(required = true, defaultValue = "model")
+ @JsonPropertyDescription("Attribute corresponding to ML model")
+ @AutofillAttributeNameOnPort1
+ var model: String = _
+
+ @JsonSchemaTitle("Target Attribute")
+ @JsonPropertyDescription("Attribute in your dataset corresponding to
target.")
+ @JsonProperty(required = true)
+ @AutofillAttributeName
+ var target: String = _
+
+ override def generatePythonCode(): String =
+s"""from pytexera import *
+ |from sklearn.metrics import accuracy_score, f1_score, precision_score,
recall_score
+ |class ProcessTupleOperator(UDFOperatorV2):
+ |@overrides
+ |def open(self) -> None:
+ |self.data = []
+ |@overrides
+ |def process_tuple(self, tuple_: Tuple, port: int) ->
Iterator[Optional[TupleLike]]:
+ |if port == 0:
+ |self.data.append(tuple_)
+ |else:
+ |model = tuple_["$model"]
+ |table = Table(self.data)
+ |Y = table["$target"]
+ |X = table.drop("$target", axis=1)
+ |predictions = model.predict(X)
+ |tuple_["accuracy"] = round(accuracy_score(Y, predictions),
4)
+ |tuple_["f1"] = f1_score(Y, predictions)
+ |tuple_["precision"] = precision_score(Y, predictions)
+ |tuple_["recall"] = recall_score(Y, predictions)
+ |yield tuple_""".stripMargin
+
+ override def operatorInfo: OperatorInfo =
+OperatorInfo(
+ "Sklearn Testing",
+ "Sklearn Testing Operator",
Review Comment:
Fixed.
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Re: [PR] feat: introduce sklearn testing operator [texera]
aicam commented on code in PR #4211:
URL: https://github.com/apache/texera/pull/4211#discussion_r2806762078
##
common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/sklearn/testing/SklearnTestingOpDesc/SklearnTestingOpDesc.scala:
##
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.sklearn.testing.SklearnTestingOpDesc
+
+import com.fasterxml.jackson.annotation.{JsonProperty, JsonPropertyDescription}
+import com.kjetland.jackson.jsonSchema.annotations.JsonSchemaTitle
+import org.apache.texera.amber.core.tuple.{AttributeType, Schema}
+import org.apache.texera.amber.core.workflow.{InputPort, OutputPort,
PortIdentity}
+import org.apache.texera.amber.operator.PythonOperatorDescriptor
+import org.apache.texera.amber.operator.metadata.annotations.{
+ AutofillAttributeName,
+ AutofillAttributeNameOnPort1
+}
+import org.apache.texera.amber.operator.metadata.{OperatorGroupConstants,
OperatorInfo}
+
+class SklearnTestingOpDesc extends PythonOperatorDescriptor {
+ @JsonSchemaTitle("Model Attribute")
+ @JsonProperty(required = true, defaultValue = "model")
+ @JsonPropertyDescription("Attribute corresponding to ML model")
+ @AutofillAttributeNameOnPort1
+ var model: String = _
+
+ @JsonSchemaTitle("Target Attribute")
+ @JsonPropertyDescription("Attribute in your dataset corresponding to
target.")
+ @JsonProperty(required = true)
+ @AutofillAttributeName
+ var target: String = _
+
+ override def generatePythonCode(): String =
+s"""from pytexera import *
+ |from sklearn.metrics import accuracy_score, f1_score, precision_score,
recall_score
+ |class ProcessTupleOperator(UDFOperatorV2):
+ |@overrides
+ |def open(self) -> None:
+ |self.data = []
+ |@overrides
+ |def process_tuple(self, tuple_: Tuple, port: int) ->
Iterator[Optional[TupleLike]]:
+ |if port == 0:
+ |self.data.append(tuple_)
+ |else:
+ |model = tuple_["$model"]
+ |table = Table(self.data)
+ |Y = table["$target"]
+ |X = table.drop("$target", axis=1)
+ |predictions = model.predict(X)
+ |tuple_["accuracy"] = round(accuracy_score(Y, predictions),
4)
+ |tuple_["f1"] = f1_score(Y, predictions)
+ |tuple_["precision"] = precision_score(Y, predictions)
+ |tuple_["recall"] = recall_score(Y, predictions)
+ |yield tuple_""".stripMargin
+
+ override def operatorInfo: OperatorInfo =
+OperatorInfo(
+ "Sklearn Testing",
+ "Sklearn Testing Operator",
Review Comment:
Could you please more informative description? like `It will generate F1,
precision, accuracy and recall for a trained Sklearn model`
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